An energy-saving data slicing method and networking system based on time-sensitive networking

Abstract

This invention proposes an energy-saving data slicing method based on time-sensitive networking, comprising the following steps: determining a standard time slice based on the acquisition cycle of each acquisition node; determining a standard data block based on the size of the data block acquired by each acquisition node; slicing time slices with acquisition cycles longer than the standard time slices, and accumulating time slices with acquisition cycles shorter than the standard time slices, so that the acquisition cycle of the time slices after slicing and accumulating is consistent with the acquisition cycle of the standard time slices; slicing data blocks with memory occupancy greater than the standard data blocks, and accumulating data blocks with memory occupancy less than the standard data blocks, so that the memory occupancy of the data blocks after slicing and accumulating is consistent with the memory occupancy of the standard data blocks, thereby improving data acquisition efficiency, shortening the time for subsequent data transmission, processing, and analysis, and improving the transmission efficiency of the entire network system.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an energy-saving data slicing method and networking system based on time-sensitive networking. Background Technology

[0002] Emerging technologies such as the Industrial Internet, autonomous vehicle networks, and intelligent health systems are all important applications of Time-Sensitive Networks (TSNs). Time-sensitive control loops are already being implemented in industrial automation equipment to address stringent latency constraints. With the development of Industry 4.0, intelligent manufacturing has become the goal leading the next generation of industrial technology. The Industrial Internet is growing in scale, and more and more production equipment used in industrial manufacturing is controlled by computer-controlled network systems. The number of sensors and actuators integrated into these networks is also increasing, forming physical control systems. These physical control systems are key components of intelligent manufacturing, and their normal operation requires corresponding network support. A new challenge has quietly emerged in the Industrial Internet: how to manage time-sensitive congestion. Many of these physical control systems have high requirements for network transmission speed. For example, the motion control of collaborative robots is highly time-sensitive, and its operation requires a low-latency transmission network.

[0003] To address the real-time communication requirements of the aforementioned applications, the IEEE Time-Sensitive Networking Task Force (TSF) developed a standard called IEEE 802.1Qbv. This standard includes a Time-Aware Shaper (TAS) that relies on a synchronization time period and a feature gate time period for pre-emptive congestion management in high-priority scenarios. The TAS aims to provide immediate packet service and, to cope with dynamic network scenarios, considers employing various reconfiguration mechanisms to dynamically allocate network resources. Specifically, given network capacity at a given boundary, it is necessary to maximize the allowable traffic flow within the network. Over the past few decades, network utility maximization has become a common strategy for maximizing network resource allocation.

[0004] Currently, when collecting and transmitting energy-saving data, the energy-saving field involves multiple data collection nodes. The frequency (cycle) of energy-saving data collection and the size of the data block collected by each node are different. This will greatly affect the efficiency of data collection, transmission and processing, and is not conducive to the high-speed and low-latency operation of the transmission network. Summary of the Invention

[0005] This invention proposes an energy-saving data slicing method and networking system based on time-sensitive networking, which solves the problem of low efficiency in data acquisition, transmission and processing in the prior art.

[0006] The technical solution of this invention is implemented as follows:

[0007] According to one aspect of the present invention, an energy-saving data slicing method based on time-sensitive networks is provided, comprising the following steps:

[0008] The standard time slice is determined based on the acquisition cycle of each acquisition node;

[0009] The standard data block is determined based on the size of the data blocks collected by each acquisition node;

[0010] Time slices with acquisition periods longer than the standard time slice are sliced, and time slices with acquisition periods shorter than the standard time slice are accumulated, so that the acquisition period of the time slices after slicing and accumulation is consistent with the acquisition period of the standard time slice.

[0011] Data blocks that occupy more memory than the standard data block are sliced, and data blocks that occupy less memory than the standard data block are accumulated, so that the memory occupied by the data blocks after slicing and accumulation is consistent with the memory occupied by the standard data block.

[0012] This invention focuses on two dimensions: time and data. It achieves time-slice segmentation of energy-saving data from different industrial field acquisition nodes and data block size splitting in the data block dimension by calculating standard time slices and standard data blocks, respectively. At the same time, it utilizes time-sensitive networking technology for time slice and data block reassembly at the data link layer to achieve efficient transmission of energy-saving data. This simplifies the data acquisition process, shortens data processing and analysis time, and further optimizes energy saving in the entire network system.

[0013] As a preferred embodiment of the present invention, the method for determining the standard time slice is as follows:

[0014] Collect the collection cycles of each collection node into a set:

[0015] , n The number of data collection nodes;

[0016] set T Calculate the difference between any two elements in the set and sort the differences in ascending order to obtain the set of differences. ;

[0017] The acquisition period for the standard time slice is:

[0018] ;

[0019] in, This is the acquisition period for a standard time slice.

[0020] As a preferred embodiment of the present invention, after determining the acquisition period of the standard time slice, the number of slices for a certain time slice is calculated using the following formula:

[0021] ;

[0022] in, p For the first i The number of time slices collected by each collection node; Indicates rounding down; For the first i The collection cycle of each collection node and , i ∈(1,2,3,…, n ).

[0023] As a preferred embodiment of the present invention, if If it is a decimal, then the sliced ​​result will be... p The data contained in each standard time slice are packaged and transmitted together. The remaining time slices after slicing are accumulated and processed until the accumulation reaches the acquisition cycle of the standard time slice, and then packaged and transmitted together.

[0024] As a preferred embodiment of the present invention, the method for determining the standard data block is as follows:

[0025] The collection consists of the memory usage of data blocks collected by each acquisition node.

[0026] , n The number of data collection nodes;

[0027] set D Calculate the difference between any two elements in the set and sort the differences in ascending order to obtain the set of differences. ;

[0028] The memory usage of a standard data block is:

[0029] ;

[0030] in, The memory usage of a standard data block.

[0031] As a preferred embodiment of the present invention, after determining the memory occupied by a standard data block, the number of slices for a certain data block is calculated using the following formula:

[0032] ;

[0033] in, q For the first i The number of data blocks collected by each acquisition node after being sliced; Indicates rounding down; For the first i Each data collection node collects data blocks that consume memory and , i ∈(1,2,3,…, n ).

[0034] As a preferred embodiment of the present invention, if If it is a decimal, then the sliced ​​result will be... q The data contained in each standard data block is packaged and transmitted together. The remaining data blocks after slicing are accumulated and processed until they reach the memory occupied by the standard data blocks, and then packaged and transmitted together.

[0035] According to another aspect of the present invention, an energy-saving data slicing networking system based on time-sensitive networking is provided, comprising:

[0036] At the field end, an edge cloud is built to process the energy consumption monitoring data collected by the field data acquisition terminal. The edge cloud processes the energy consumption monitoring data collected by the field data acquisition terminal by executing the above-mentioned slicing method.

[0037] The data storage terminal, which communicates with the field terminal, receives and stores the energy consumption monitoring data processed by the edge cloud at the field terminal by building a public cloud.

[0038] The remote platform that communicates with the field terminal interacts with the edge cloud of the field terminal by building a private cloud for data flow and control flow.

[0039] The monitoring platform monitors the energy consumption of various energy-consuming enterprises by accessing public and private clouds.

[0040] As a preferred embodiment of the present invention, the field data acquisition terminal includes:

[0041] Node-level devices, built into energy-consuming equipment, are used to monitor and collect real-time energy consumption data and equipment operation data of the energy-consuming equipment;

[0042] Workshop-level devices, built into storage devices in various workshops of the factory, are used to collect and store energy consumption data collected by each node-level device;

[0043] Factory-level devices, built into the factory's storage server, are used to store the energy consumption data of each workshop-level device, providing a computable energy consumption data source for the edge cloud.

[0044] According to another aspect of the present invention, a storage medium is provided storing a computer program, which is executed by a processor to implement the steps of the above-described slicing method. Beneficial effects

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention determines the standard time slice and standard data block by calculation, and uses the standard time slice and standard data block as a reference to split and accumulate the time slice and data block collected by each acquisition point. This makes the acquisition period of the time slice after slicing and accumulation consistent with the acquisition period of the standard time slice, and makes the memory occupied by the data block after slicing and accumulation consistent with the memory occupied by the standard data block. This improves the data acquisition efficiency, shortens the subsequent data transmission, processing and analysis time, and improves the transmission efficiency of the entire network system. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating an energy-saving data slicing method based on time-sensitive networks according to the present invention.

[0048] Figure 2 This is a schematic diagram of a tree-like model showing the distribution of data collection nodes in relation to geographical location in an embodiment of the present invention.

[0049] Figure 3 This is an architecture diagram of an energy-saving data slicing networking system based on time-sensitive networking according to the present invention. Detailed Implementation

[0050] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0051] Reference Figure 1 As shown, this embodiment provides an energy-saving data slicing method based on time-sensitive networks, including the following steps:

[0052] The standard time slice is determined based on the acquisition cycle of each acquisition node;

[0053] The standard data block is determined based on the size of the data blocks collected by each acquisition node;

[0054] Time slices with acquisition periods longer than the standard time slice are sliced, and time slices with acquisition periods shorter than the standard time slice are accumulated, so that the acquisition period of the time slices after slicing and accumulation is consistent with the acquisition period of the standard time slice.

[0055] Data blocks that occupy more memory than the standard data block are sliced, and data blocks that occupy less memory than the standard data block are accumulated, so that the memory occupied by the data blocks after slicing and accumulation is consistent with the memory occupied by the standard data block.

[0056] In this embodiment, since the distribution of data acquisition nodes is related to the geographical distribution of their respective equipment, workshops, factories, or enterprises, these factors should be comprehensively considered during the process. Simultaneously, these factors also determine the selection of networking equipment and components, the allocation of network bandwidth, and the establishment of network topology and networking type. Therefore, adopting methods such as... Figure 2 The tree model shown describes the process of determining the standard time slice and its relationship with these factors. Figure 2 In this context, the collection of affiliated enterprises or factories is... Workshop assembly Equipment collection The set of data collection nodes is ; Figure 2 middle, Indicates the first n The first factory (or enterprise), the first i The workshop, the first j The device, the first k Data collected by each data acquisition node. The data acquisition nodes are determined based on their distribution and the geographical distribution of the equipment, workshops, factories, or enterprises to which they belong. k The time slice size (i.e., the acquisition period).

[0057] As a preferred embodiment, the method for determining the standard time slice is as follows:

[0058] Collect the collection cycles of each collection node into a set:

[0059] , n The number of data collection nodes;

[0060] set T Calculate the difference between any two elements in the set and sort the differences in ascending order to obtain the set of differences. ;

[0061] The acquisition period for the standard time slice is:

[0062] ;

[0063] in, This is the acquisition period for a standard time slice.

[0064] As a preferred embodiment, after determining the acquisition period of the standard time slice, the number of slices for a certain time slice is calculated using the following formula:

[0065] ;

[0066] in, p For the first i The number of time slices collected by each collection node; Indicates rounding down; For the first i The collection cycle of each collection node and , i ∈(1,2,3,…, n ).

[0067] As a preferred embodiment, if If it is a decimal, then the sliced ​​result will be... p The data contained in each standard time slice are packaged and transmitted together. The remaining time slices after slicing are accumulated and processed until the accumulation reaches the acquisition cycle of the standard time slice, and then packaged and transmitted together.

[0068] In this embodiment, it is assumed that the acquisition periods of the five acquisition nodes with different acquisition periods (assuming that the geographical distribution influencing factors are the same) are 5 seconds, 10 seconds, 25 seconds, 40 seconds, and 60 seconds, respectively.

[0069] T ={5,10,25,50,60}, then,

[0070] Δ T ={5,10,15,20,25,35,40,45,50,55};

[0071] The acquisition period for a standard time slice is:

[0072] (5 + 55) / 2 = 30 seconds;

[0073] The time slice with a sampling period of 60 seconds yields two standard time slices, and the time slice with a sampling period of 50 seconds yields one standard time slice and one time slice with a sampling period of 20 seconds. The remaining time slices with sampling periods of 5 seconds, 10 seconds, 20 seconds, and 25 seconds are accumulated to obtain two standard time slices. Finally, all the standard time slices are packaged together and transmitted.

[0074] In this embodiment, due to differences in the type and manufacturer of the data acquisition nodes, and the varying number installed in different workshops and on different equipment, the size of the data blocks generated per unit time varies depending on the equipment or workshop used for aggregation. Therefore, these factors should be comprehensively considered during the data acquisition process.

[0075] At the same time, these factors also determine the selection of networking equipment and components, the allocation of networking bandwidth, and the establishment of network topology and networking type. Therefore, similarly, the following can be adopted... Figure 2 The tree model shown describes the process of determining the standard data block size and the relationship between these factors. However, unlike the determination and calculation of standard time slices, the determination of standard data blocks is related to factors such as the amount of data generated per unit time by different node types, the number of nodes, and the cumulative time.

[0076] As a preferred embodiment, the method for determining the standard data block is as follows:

[0077] The collection consists of the memory usage of data blocks collected by each acquisition node.

[0078] , n The number of data collection nodes;

[0079] set D Calculate the difference between any two elements in the set and sort the differences in ascending order to obtain the set of differences. ;

[0080] The memory usage of a standard data block is:

[0081] ;

[0082] in, The memory usage of a standard data block.

[0083] As a preferred embodiment, after determining the memory occupied by a standard data block, the number of slices for a certain data block is calculated using the following formula:

[0084] ;

[0085] in, q For the first i The number of data blocks collected by each acquisition node after being sliced; Indicates rounding down; For the first i Each data collection node collects data blocks that consume memory and , i ∈(1,2,3,…, n ).

[0086] As a preferred embodiment, if If it is a decimal, then the sliced ​​result will be... q The data contained in each standard data block is packaged and transmitted together. The remaining data blocks after slicing are accumulated and processed until they reach the memory occupied by the standard data blocks, and then packaged and transmitted together.

[0087] In this embodiment, assuming that the data block sizes collected by five different nodes within a specified statistical time period (assuming no difference in geographical distribution influencing factors) are 100 MB, 350 MB, 650 MB, 900 MB, and 1000 MB respectively, then,

[0088] D ={100,350,650,900,1000}, then,

[0089] Δ D ={100,250,300,350,550,650,800,900};

[0090] The memory usage of a standard data block is:

[0091] (100+900) / 2=500 trillion;

[0092] A data block occupying 1000 MB of memory is sliced ​​to obtain two standard data blocks. A data block occupying 900 MB of memory is sliced ​​to obtain one standard data block and one data block occupying 400 MB of memory. A data block occupying 650 MB of memory is sliced ​​to obtain one standard data block and one data block occupying 150 MB of memory. The remaining data blocks occupying 100 MB, 150 MB, 350 MB, and 400 MB of memory are accumulated to obtain two standard data blocks. Finally, all the standard data blocks are packaged together and transmitted.

[0093] Accordingly, such as Figure 3 As shown, this embodiment also provides an energy-saving data slicing networking system based on time-sensitive networking, including:

[0094] At the field end, an edge cloud is built to process the energy consumption monitoring data collected by the field data acquisition terminal. The edge cloud processes the energy consumption monitoring data collected by the field data acquisition terminal by executing the above-mentioned slicing method.

[0095] The data storage terminal, which communicates with the field terminal, receives and stores the energy consumption monitoring data processed by the edge cloud of the field terminal through the construction of a public cloud. The public cloud is essentially a large-scale, massive data storage platform whose main function is data storage and auxiliary computing functions.

[0096] The remote platform, which communicates with the field, interacts with the edge cloud of the field through the establishment of a private cloud for data and control flow. The private cloud is essentially a self-built private cloud server cluster used to run the energy consumption monitoring service platform. Its function is to store and transmit control and decision-making data information for the edge cloud computing server cluster in the industrial field. The edge cloud computing server cluster then performs policy control and data transmission and reception for industrial Internet devices such as servers, IoT middleware, and acquisition terminals at various levels.

[0097] The regulatory platform monitors the energy consumption of various energy-consuming enterprises by accessing public and private clouds. It is a data-driven decision-making service platform set up by energy-saving industry service solution providers for energy regulatory departments to monitor the energy consumption and carbon emissions of energy-consuming enterprises. With the help of this platform, regulatory departments can read the real-time energy consumption data of various energy-consuming enterprises from the public cloud through a dedicated network.

[0098] Energy-saving service companies can use the energy-saving networking system ("four terminals and three clouds") architecture to collect, store, and analyze real-time energy consumption data of energy-consuming enterprises. They can also provide standardized energy-saving services for energy-consuming enterprises of different industries, sizes, and industrial site environments. Therefore, this architecture can provide practical application reference value for advanced industrial Internet networking and communication technologies.

[0099] As a preferred embodiment of the present invention, the field data acquisition terminal includes:

[0100] Node-level devices, built into energy-consuming equipment, are used to monitor and collect real-time energy consumption data and equipment operation data of energy-consuming equipment, making it easier to implement remote control and energy-saving optimization strategies for equipment that has abnormal energy consumption, abnormal operation, or changes in operating status;

[0101] Workshop-level devices are built into storage devices in various workshops of the factory. They are used to collect and store energy consumption data collected by each node-level device, which facilitates the data to be stored and aggregated on enterprise (factory)-level devices.

[0102] Factory-level devices, built into the factory's storage server, are used to store the energy consumption data of each workshop-level device, providing a computable energy consumption data source for the edge cloud.

[0103] Accordingly, this embodiment also provides a storage medium storing a computer program, which is executed by a processor to implement the steps of the above-described slicing method.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An energy-saving data slicing method based on time-sensitive networks, characterized in that, Includes the following steps: The size of the standard time slice is determined based on the acquisition cycle of each acquisition node. The method for determining the size of the standard time slice is as follows: Collect the collection cycles of each collection node into a set: , n The number of data collection nodes; set T Calculate the difference between any two elements in the set and sort the differences in ascending order to obtain the set of differences. ; The size of the standard time slice is: ； in, This is the size of a standard time slice; The memory size of the standard data block is determined based on the memory size of the data blocks collected by each acquisition node. The method for determining the memory size of the standard data block is as follows: The collection consists of the memory usage of data blocks collected by each acquisition node. , n The number of data collection nodes; set D Calculate the difference between any two elements in the set and sort the differences in ascending order to obtain the set of differences. ; The memory size occupied by a standard data block is: ； in, This refers to the memory size occupied by a standard data block. Time slices with acquisition periods longer than the standard time slice are sliced, and time slices with acquisition periods shorter than the standard time slice are accumulated, so that the acquisition period of the time slices after slicing and accumulation is consistent with the size of the standard time slice. Data blocks whose memory usage is greater than that of standard data blocks are sliced, and data blocks whose memory usage is less than that of standard data blocks are accumulated, so that the memory usage of the data blocks after slicing and accumulation is consistent with that of standard data blocks. The acquisition nodes include node-level devices, workshop-level devices, and factory-level devices.

2. The energy-saving data slicing method based on time-sensitive networks as described in claim 1, characterized in that, After determining the size of the standard time slice, the number of slices for a given time slice is calculated using the following formula: ； in, p For the first i The number of time slices collected by each collection node; Indicates rounding down; For the first i The collection cycle of each collection node and , i ∈(1,2,3,…, n ).

3. The energy-saving data slicing method based on time-sensitive networks as described in claim 2, characterized in that, like If it is a decimal, then the sliced ​​result will be... p The data contained in each standard time slice is packaged and transmitted uniformly, and the remaining time slices after slicing are accumulated and processed.

4. The energy-saving data slicing method based on time-sensitive networks as described in claim 1, characterized in that, After determining the memory size of a standard data block, the number of slices for a given data block is calculated using the following formula: ； in, q For the first i The number of data blocks collected by each acquisition node after being sliced; Indicates rounding down; For the first i The memory size occupied by the data blocks collected by each acquisition node and , i ∈(1,2,3,…, n ).

5. The energy-saving data slicing method based on time-sensitive networks as described in claim 4, characterized in that, like If it is a decimal, then the sliced ​​result will be... q The data contained in each standard data block is uniformly packaged and transmitted, and the remaining data blocks after slicing are accumulated and processed.

6. An energy-saving data slicing networking system based on time-sensitive networking, characterized in that, include: At the field end, an edge cloud is built to process the energy consumption monitoring data collected by the field data acquisition terminal. The edge cloud processes the energy consumption monitoring data collected by the field data acquisition terminal by executing the slicing method according to any one of claims 1 to 5. The data storage terminal, which communicates with the field terminal, receives and stores the energy consumption monitoring data processed by the edge cloud at the field terminal by building a public cloud. The remote platform that communicates with the field terminal interacts with the edge cloud of the field terminal by building a private cloud for data flow and control flow. The monitoring platform monitors the energy consumption of various energy-consuming enterprises by accessing public and private clouds.

7. The energy-saving data slicing networking system based on time-sensitive networking as described in claim 6, characterized in that, The on-site data acquisition terminal includes: Node-level devices, built into energy-consuming equipment, are used to monitor and collect real-time energy consumption data and equipment operation data of the energy-consuming equipment; Workshop-level devices, built into storage devices in various workshops of the factory, are used to collect and store energy consumption data collected by each node-level device; Factory-level devices, built into the factory's storage server, are used to store the energy consumption data of each workshop-level device, providing a computable energy consumption data source for the edge cloud.

8. A storage medium storing a computer program, characterized in that, The computer program is executed by a processor to implement the steps of the slicing method according to any one of claims 1 to 5.

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